Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system

Niu, Wenqing; Ha, Yinaer; Chi, Nan

doi:10.1007/s11432-019-2850-3

Cited by 14 publications

(6 citation statements)

References 18 publications

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Section: Introductionmentioning

confidence: 99%

“…It has been also investigated the classification problems to improve the communication performance in VLC systems [33][34][35][36][37]. Niu et al suggested a SVM based system to reduce signal distortion in the VLC channel and optical fiber for constellation classification of geometrically-shaped 8QAM [33]. According to experimental results, it is shown that it can be achieved transmission at −2.5 dBm input optical power under the 7% forward error correction (FEC) threshold.…”

Section: Introductionmentioning

confidence: 99%

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The modulation classification methods in PPM–VLC systems

Ağır

Sönmez

2023

Opt Quant Electron

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Intelligent methods have been applied to many fields for a long time. Recently, Visible Light Communication (VLC) systems widely include learning and classification models to improve their performances. The classification of L-Pulse Position Modulation (L-PPM) formats is crucial for VLC systems since the modulation order L is very effective for providing energy efficiency and increasing the transmission capacity. In this paper, therefore, it is reported for the first time, the classification of PPM schemes in VLC systems by using Decision Tree, Knearest neighbor (KNN), Support Vector Machine (SVM), and a Direct Decision-based Linear Model (LM) technique. A novel feature extraction model is derived to be able to classify the type of PPM modulation schemes. A comparison has been given to observe the performance of classification schemes by taking into account the level of Signal to Noise Ratio and the transmission distance between receiver and transmitter. The KNN method gives the best accuracy performance against other schemes at the SNR of 25dB and the distance of 2.32m and more, while the Tree model is superior to KNN at the distances of 2.25m and 2.20m. Additionally, it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20m.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The modulation classification methods in PPM–VLC systems

Ağır

Sönmez

2023

Opt Quant Electron

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“…SVM utilized optical barcode detection technique based on VLC have been studied in [ 23 ]. In [ 24 ], the authors have described SVM for constellation classification in two kinds of geometrically shaped 8-quadrature amplitude modulation for seamlessly integrated fiber and the VLC system. However, in all the traditional detection schemes, all the detected LEDs have been considered as data transmitting LEDs.…”

Section: Introductionmentioning

confidence: 99%

Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication

Rahman

Shahjalal

Hasan

et al. 2021

Sensors

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Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

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“…With the rapid development of computer technology, intelligent algorithms have been applied extensively to structural damage identification and yielded satisfactory results 8–10 . The SVM can be used to solve nonlinearity, high‐dimension, and local minima problems 11–14 . Diao et al 15 proposed a novel damage identification method based on transmissibility function and SVM.…”

Section: Introductionmentioning

confidence: 99%

Modal‐based identification method of fire damage in reinforced concrete T‐beams using support vector machine and firefly algorithm

Liu

Song

Liu

et al. 2021

Struct Control Health Monit

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Summary To determine the degree of fire damage in reinforced concrete (RC) T‐beams, a damage identification method based on an improved support vector machine and firefly algorithm (FA‐SVM) is proposed herein. First, based on the fire test of 10 simply supported beams, a refined finite element model of simply supported T‐beams was established. A modal analysis of the simply supported beams was performed to obtain a sample library of the input and output parameters for the FA‐SVM identification network. Subsequently, the trained FA‐SVM identification network was used to predict the fire exposure duration of the samples. The sectional temperature during the fire exposure duration could be calculated by using the finite element model, and the bending stiffness and bending capacity of the simply supported beams after exposure to the fire were calculated. The test sample results were similar to the experimental results of the simply supported beams exposed to fire for 60, 90, and 120 min, which demonstrated the feasibility and effectiveness of the proposed method. Finally, a three‐step method for fire damage identification suitable for RC continuous beams was developed based on the FA‐SVM identification network. An example calculation analysis of three‐span continuous beams was performed, and the results demonstrated accuracy of the identification results. The identification sample magnitude was significantly reduced using this method, which can be conveniently used in practical engineering applications.

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Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system

Cited by 14 publications

References 18 publications

The modulation classification methods in PPM–VLC systems

The modulation classification methods in PPM–VLC systems

Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication

Modal‐based identification method of fire damage in reinforced concrete T‐beams using support vector machine and firefly algorithm

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